Method and apparatus for producing wind energy with reduced wind turbine noise

ABSTRACT

A method for controlling noise from a wind park that has a plurality of wind turbines includes monitoring noise emission from the wind turbines in at least a near field area and utilizing a transfer function of noise emission to determine a noise impact importance of the wind turbines at one or more locations in a far field area beyond a boundary of the wind park. The method further includes determining which, if any, wind turbines to operate in a noise-reduced operation mode in accordance with the noise impact importance determination and controlling operation modes of the wind turbines in accordance with the determination of which, if any, wind turbines to operate in a noise reduced mode.

BACKGROUND OF THE INVENTION

This invention relates generally to power generation systems and moreparticularly to methods and apparatus for reducing noise impact fromwind turbine installations.

In many parts of the world, the issuance of permits for wind turbineinstallations is based upon the environmental noise impact effected orpotentially effected by the installation. For example, in most Europeancountries, a maximum (fixed) sound pressure level is set by type of landarea (agricultural, industrial, residential, or other) in the vicinityof wind turbine park boundaries, or in some noise regulation cases, theapproach of industrial noise relative emergence is favored. Noiseemergence is defined herein as the relative noise level increase relatedto an industrial installation with reference to the initial, existingbackground noise level.

At least three known methods exist for controlling noise from windturbine installations. In a first known method described in U.S. Pat.No. 6,688,841, permanent noise monitoring is based on a maximum absolutenoise level, regardless of the noise contribution of the wind turbine orturbines. Thus, a wind park may be forced to run in a reduced noiseoperating mode irrespective of the contribution to noise made by thewind turbine or turbines. For example, a reduced noise operating modemay be entered when a vehicle on the road, a tractor, a heating,ventilation, and air conditioning (HVAC) system or other deviceoperating in the vicinity of the monitoring system produces excessnoise.

In another known noise reduction method, permanent noise monitoringbased on wind direction noise projection is used. However, this methodmay not necessarily lead to proper turbine noise reduction because windturbine rotor noise exhibits sound directivity not necessarily maximumin down-wind direction. Also, wind gradient induced acoustic convectioncan be of least importance compared to sound propagation celerity oreven compared to large atmospheric air temperature gradient inducedacoustic refraction effects. Thus, wind direction statistics may notadequately address relevant noise contributions.

In yet another known noise reduction method, control is based on asimplified atmospheric sound propagation model (ISO 9613). This methodmay lead to dramatic energy capture loss as atmospheric propagationeffects above mentioned (air temperature gradient, wind gradient, andsite topography) are not taken into account. Moreover, a fixed basicacoustic model does not dynamically adapt to changing site backgroundacoustic conditions.

BRIEF DESCRIPTION OF THE INVENTION

In one aspect, the present invention therefore provides a method forcontrolling noise from a wind park that includes a plurality of windturbines. The method includes monitoring noise emission from the windturbines in at least a near field area and utilizing a transfer functionof noise emission to determine a noise impact importance of the windturbines at one or more locations in a far field area beyond a boundaryof the wind park. The method further includes determining which, if any,wind turbines to operate in a noise-reduced operation mode in accordancewith the noise impact importance determination and controlling operationmodes of the wind turbines in accordance with the determination ofwhich, if any, wind turbines to operate in a noise reduced mode.

In another aspect, the present invention provides an apparatus forreducing noise impact from a wind park having a plurality of windturbines. The apparatus includes at least one site reference microphoneand a plurality of site boundary area microphones, and a computerconfigured to monitor noise emission from the wind turbines utilizingthe site reference microphone(s) and the plurality of site boundarymicrophones. The computer is further configured to utilize a transferfunction of noise emission to determine a noise impact importance of thewind turbines at one or more locations in the far field beyond aboundary of the wind park and to determine which, if any, wind turbinesto operate in a noise-reduced operation mode in accordance with thenoise impact importance determination. The computer is furtherconfigured to control operation modes of the wind turbines in accordancewith the determination of which, if any, wind turbines to operate in anoise reduced mode.

In yet another aspect, the present invention provides a wind park forgenerating electrical energy at a reduced far field area noise impact.The wind park includes a plurality of wind turbines, at least one sitereference microphone and a plurality of site boundary or far field areamicrophones, and a computer system configured to monitor noise emissionfrom the wind turbines utilizing the site reference microphone(s) andthe plurality of site boundary microphones. The computer system isfurther configured to utilize a transfer function of noise emission todetermine a noise impact importance of the wind turbines at one or morelocations in a far field area beyond a boundary of the wind park, and todetermine which, if any, wind turbines to operate in a noise-reducedoperation mode in accordance with the noise impact importancedetermination. The computer is further configured to control operationmodes of the wind turbines in accordance with the determination ofwhich, if any, wind turbines to operate in a noise reduced mode.

It will thus be appreciated that various configurations of the presentinvention permit noise far field area noise impact (immission) of a windturbine park to be controlled while allowing increased electrical energyoutput from the wind turbine park relative to known methods and systemsfor controlling noise.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a map of a wind turbine park configuration of the presentinvention.

FIG. 2 is a map of the wind turbine park configuration of FIG. 1,showing locations of relatively persistent road noise.

FIG. 3 is a map of the wind turbine park configuration of FIG. 1,showing locations of high and enhanced noise sensitivity.

FIG. 4 is a drawing of a typical wind turbine time spectrum noiseanalysis chart.

FIG. 5 is a schematic block diagram of a signal acquisition systemconfiguration used in some configurations of the present invention.

FIG. 6 is a schematic block diagram of a configuration of a controlnetwork of the present invention for analyzing and controlling noisefrom a wind turbine park.

DETAILED DESCRIPTION OF THE INVENTION

As used herein, “emission” with an “e” is a machinery or product noisegeneration that is attributed tot his machinery or product an can bemeasured in the near field area to determine the corresponding generatedacoustic power (the measured quantity being the sound power level).“Immission” with an “I” is the far field area noise impact, oftenmeasured and qualified by noise map as a result of the combination ofnoise sources making noise in the far field with respect to any previousexisting background noise map. Emission is thus a source term, whileimmission is the consequence of emission, i.e., the noise footprint orimpact within the reception area. Also, as used herein, the terms “windturbine park” and “wind park” are considered synonymous.

Technical effects of some configurations of the present inventioninclude the reduction of noise impact produced by a wind park having aplurality of wind turbines, while enabling increased power productionfrom the wind park. The manner in which these technical effects areachieved will become apparent from the description below.

In some configurations of the present invention and referring to FIG. 1,a wind turbine park 100 comprises a plurality of wind turbines, some ofwhich are labeled as wind turbines 102. Adaptive noise control used insome configurations of the present invention to selectively reduce noiseimpact from wind parks 100 only when an emerging contribution isdetected. Noise signature recognition in some configurations is providedin some configurations by time and frequency signal analysis, whereinwind turbine 102 noise is recognized and differentiated from backgroundnoise. In some configurations, the noise recognition system uses a noisesignature difference and differential noise level rather than absolutenoise levels. An adaptive, self-learning algorithm implicitly integrateslarge noise atmospheric propagation variations that are not supported bynoise reduction techniques that assume “fixed weather model.” Advantageis take of extraneous background noises and favorable atmosphericconditions, when possible, to mask or reduce wind turbine noise, so thatwind turbines do not have to be put in a reduced noise operating modewhen ambient conditions are favorable. Noise impact requirements andsite sensitivity requirements can be met with various configurations ofthe present invention and noise impact can be determined periodically(i.e., yearly) and compared to or computed as a function of energy yieldloss. An adaptive, self-learning neural network algorithm can detect andqualify acoustic transfer functions from near field microphones and farfield microphones in order to determine individual wind turbine noisecontribution based on energy, time and frequency analysis (acousticsignature) as well as best noise reduction scenario in a dynamic andadaptive manner.

Adaptive selective noise control can be used for achieving specificnoise-reduction targets. For example, some configurations of the presentinvention can be used to control wind turbines 102 as a function ofglobal noise emergence (i.e., sound power contribution) and sensitivitymapping (i.e., a reception area weighting factor). Some configurationsof the present invention can be used to avoid unnecessarily high energyloss that would otherwise be suffered as a result of attempting toreduce noise using a fixed noise reduction method (e.g., the setting ofmaximum absolute levels and maximum operation levels). Someconfigurations can be used for both purposes.

More particularly, in some configurations of the present invention, in anear field 104 of wind turbine park 100, a near-field noise ofrelatively high intensity is created when wind turbines 102 are inoperation. The noise from wind turbines 102 can generally becharacterized as a repetitive “shwoosh-shwoosh” sound as each turbine102 rotates. A contour 106 representing an approximate boundary of windturbine park 100 and enclosing an area of lesser noise intensitysurrounds near field 104. In some configurations of the presentinvention, noise emission from wind turbines 102 are monitored utilizingpermanent monitoring outdoor microphones. The permanent monitoringoutdoor microphones include reference position microphones 108, whichare outdoor microphones located installed in a near field inside windpark 100 to monitor noise emission in a near field. The permanentmonitoring outdoor microphones also include boundary positionmicrophones 110, which are outdoor microphones located slightly beyondboundary limits 104 of wind park 100.

Ground level weather stations are not required in all configurations ofthe present invention, however, in some configurations, ground level(i.e., up to about 4 meters above ground) weather stations (not shown inFIG. 1) can optionally be installed with reference position microphones108. Additional sensitive area microphones 112 and/or addition groundlevel weather stations (not shown in FIG. 1) can optionally be installedat or near particular impact areas 114, which may include, for example,residential areas. As a baseline to an optional ground weather station,a signal from an anemometer and an outside temperature probe on eachwind turbine 102 can be used as an “altitude” weather station at hubheight. The combination of a built-in wind turbine hub height altitudeweather station and optional ground weather station can allowmeasurements of temperature gradient as a function of height.

In some configurations of the present invention, a near field referencemicrophone 108 is mounted directly on one or more of the wind turbines102 of wind park 100. Thus, not only are microphones 110 (and in someconfigurations, 114) provided in the far field beyond boundary 104 ofwind park 100 but also microphones 108 are provided in the near field.Far field microphones are configured to measure immissions (noiseimpact), near field are configured to measure emissions (noisegeneration), and sensitive area microphones are configured to measureimmissions in sensitive areas.

In some configurations of the present invention, an estimate ofbackground noise is also used to determine noise impact importance ofwind turbines 102. For example, a background noise signature assessmentis established by a neural network configured to recognize extraneousnoise sources when wind turbines 102 are not operating and for shortsampling periods on windy days and nights to assess seasonal, daily andhourly site wind background masking noise. Background masking noise caninclude, for example, road noise, vegetation wind noise, turbulent windground noise, park noise, and industrial noise, as well as other noisenot related to the wind turbine or turbines. The background noiseestimate may vary as a function of time of year and/or time of day.

For example, in some configurations of the present invention, referenceposition microphones 108 are used to establish wind turbine noisesignatures for each wind turbine 102 in wind park 100 and to establish acomparison with boundary position microphones 110 to provide noisecorrelation (e.g., for the purpose of determining a noise transferfunction as a function of direction, atmospheric conditions, and/orother variables). Noise signatures can depend upon blade type and alsoon the pitch of the blades of a wind turbine 102. Thus, referenceposition microphones 108 can be used with the noise transfer function todetermine a noise impact importance of wind turbines 102 and todetermine which, if any, wind turbines 102 to operate in a noise reducedmode in accordance with the noise impact importance determination. Inone method, for example, signatures can be determined using acousticsignature signal analysis using energy, time, frequency analysisregarding wind turbine related noise and differentiate from wind parkextraneous noise sources such as turbulent wind ground noise, vegetationwind noise, road noise, aircraft noise, agricultural machinery noise,industrial noise, and other extraneous noise sources not related to thewind turbines. In another method, a differential noise analysisdetermines the difference between wind turbine related noise and allextraneous noises levels is permanently evaluated. This difference isalso referred as wind turbine nose emergence, the primary acoustictarget for noise reduction. One method for operating the wind turbine innoise-reduction mode includes sending control commands and instructionsto individual wind turbines to fit to primary and secondary acoustictargets.

For example, and referring to FIG. 2, a relatively persistent road noisecan be expected around major highways and roads 116, as indicated by acontour 118 enclosing a high road noise region, and a contour 120enclosing between itself and contour 118 enclosing a moderate road noiseregion. Road noise is a part of existing background noise andpsychoacoustically masks wind turbine noise. Background noise or othertypes may be present, depending upon the location of wind park 100. Forexample, background noise may include any combination of noise sources,including road noise, vegetation wind noise (e.g., noise blowing throughtrees), turbulent wind ground noise (i.e., noise resulting fromturbulent wind flows near the ground), park noise (e.g., wind noise fromwind blowing through cultivated fields, animal noise, and/or parkmachinery noise), or industrial noise (noise from industrial plants).Because noise from these sources tends to be repetitive,quasi-persistent, and fixed in location, a neural network can be used tolearn and to recognize these extraneous noise sources in a mannerdescribed below.

In addition, in some configurations of the present invention andreferring to FIG. 3, noise sensitivity may be greater in some areas thanin others. For example, an area 124 having a high concentration ofresidences may be targeted as an area of high residential noisesensitivity. Additional areas, for example, an area 126 surrounding area124 in the example shown in FIG. 3, may comprise an area of somewhatenhanced residential noise sensitivity. In general, there may be anynumber of such areas, not necessarily surrounding one another, aroundany given wind park 100. The noise impact importance are, in someconfigurations, weighted (for example, by weighting an effectivemicrophone amplification) in accordance with location using a noiseimpact map. The weighting in some configurations is also time-dependent,as typical acceptable noise levels near residential areas or typicalnoise emergence levels over specific time periods can vary in accordancewith, for example, time of day, day of week, or time of year. Some areasin the map in the vicinity of the wind park are determined to becritical noise sensitive areas in the far field. These areas receivespecific and stringent local acoustic targets or goals and are secondaryacoustic targets for noise reduction.

Some configurations of the present invention recognize wind turbinenoise impact to minimize or at least reduce energy yield loss. Forexample, in some configurations, wind turbine 102 loss is recognized inthe far field by periodic impulsivity noise, i.e., a “swoosh-swoosh”related to blade noise rotation. Also, in some configurations, windturbine noise is recognized in the far field when its noise spectrumdiffers from site typical background noise, i.e., there is a differencein spectral content of the noise. More particularly, and to referring toa typical wind turbine time spectrum analysis chart shown in FIG. 4, itcan be seen that wind turbine blade aerodynamic noise 128 is of animpulsive, repetitive nature due to blade rotation. Rotating noiseconvection propagation effects also tend to make the sound be perceivedas pulsating. The measurement example shown in FIG. 3 indicates thatpressure variations in a typical wind condition are on the order of 3 to6 dB with a period on the order of 1 second for a large wind turbine,which is the blade passing frequency in the typical wind condition. Suchperiodic, impulsive noise signatures can be recognized by, for example,a neural network, in order to discriminate between noise sources ofdifferent types of noise sources having a different acoustic signature.Noise sources having different acoustic signatures can include differentwind turbines 102 in a wind park, which may also have a differenttransfer function (i.e., a different relationship between a noisegenerated by a particular wind turbine 102 in reference positionmicrophone 108 and the noise propagated to a boundary positionmicrophone 110 or to another location).

In some configurations and referring to FIG. 5, a signal acquisitionsystem comprises a plurality of sensors (e.g., sensors 108, 110, and138) located at various positions around wind park 100. For example, aplurality of wind turbines 102 comprise wind park 100 in FIG. 5. Sitereference microphones 108 are provided within wind park 100 to record anoise generated by wind turbines 102 when in operation. A plurality ofsite boundary microphones 110 are also provided, in part to measure acorrelation between noise generated by wind turbines 102 at sitereference microphones 108 and at locations at which site boundarymicrophones 110 are located. Weather stations 138 are also provided insome configurations to provide weather data indicative of atmosphericvariations within a region 122 (see FIG. 3) within which control of windturbine noise is of interest. In configurations in which weather data isavailable, the weather data can be utilized along with a stratifiedatmospheric noise propagation model in making noise impact importancedetermination. An advantage offered by some configurations of thepresent invention is that atmospheric propagation varies dramaticallywith temperature, so that every time a specially stratified atmosphereoccurs, propagation of noise takes a curved path that bends towardscolder temperature. During daytime in the summer, when there is very hotground because of the sun beaming down but fresh air at 100 meters,sound waves bend up towards the sky. This effect is favorable for a windpark operator, because most of the noise energy will tend towards thesky rather than towards the ground. In the evening of the same day, theair may cool dramatically, and with natural convection from the hotground, a temperature inversion may occur. Temperature inversions tendto be strong at the end of the summer and autumn, with fresh air nearthe ground and hot air at higher altitudes, e.g., 100 meters. At suchtimes, sound waves are bent to the ground, making a wind park 100installation appear louder than it was during the day. Thus, noiseimpact may depend on the hour of the day and on the season.

Noise variation due to temperature gradients can be as high as 10 to 20dB, and noise variation due to wind variations can be another 5 to 10dB. Dry air at 20% humidity can cause frequency-dependent variationsthat can be as high as a 10 dB absorption at 2 kHz, with a minimumabsorption at high humidity over 1000 meters. A worst case can beassumed to be about 1 dB absorption every 100 meters.

Background noise depends upon ambient conditions and can be learned. Animportant feature of configurations of the present invention is thatsome configurations take advantage of huge variations in attenuationrelating to atmospheric conditions (e.g., especially temperature andwinds) to increase the allowable power generated by one or more windturbines at a wind park. This advantage occurs because it is notnecessary to operate at worst-case noise reduction conditions in mostinstances as the result of the sensing of and knowledge gained aboutnoise propagation.

Data from site boundary microphones 110 and from site referencemicrophones 108 (and from weather stations 138, if present) iscommunicated via a local area network 130 via a digital signal bus to acomputer 132, which serves as a calculation and control server. An enduser terminal 134 is also provided to allow users to view dataconcerning the operation of wind park 100, including wind turbines 102,measured noise levels, and, where available, atmospheric conditions. Anintranet 136 may also be provided to allow more widespread viewing.Additionally, end user terminal 134 and/or intranet 136 may be used toset limits for noise impact produced by wind park 100.

Either or both local area network 130 or intranet 136 may be replaced oraugmented by other suitable networks, including wired and wirelessnetworks and the Internet, in some configurations. In the event data istransferred over the Internet, some configurations provide additionalsecurity, such as encrypted data transmission, to prevent unauthorizedcontrol of wind park 100 and/or unauthorized interception of data.

Computer 132 in some configurations serves as a centralized data andcalculation server, and can include signal analysis modules, a neuralnetwork module, and a control instruction provider module. In someconfigurations, intranet 136 can be accessed remotely only by aprivileged user to perform system control, programming updates, and/oradvanced analysis. Display 134 or a remote display may be used in someconfigurations to generate a display for an end user that includes areport of noise impact versus energy yield loss. In some configurations,a cost-efficient personal computer (PC) based multiple channel signalprocessing system is provided as computer 132.

More particularly, in some configurations and referring to FIG. 6, windpark 100 includes a plurality of wind turbines 102 and referenceposition microphones 108. Each reference position microphone 102 isprovided with an audio signal A/D converter 140, which converts ananalog noise signal received by microphone 102 into a digital signal,which is applied to a digital signal bus by a digital signal businterface circuit 142. In configurations in which weather stations 138are also provided at reference position microphones 108, weather data isalso applied to a digital signal bus using an interface circuit 142. Insome configurations, weather stations 138 may be provided. Inconfigurations in which weather stations 138 are provided that generateanalog signals, a weather signal A/D converter 144 is provided, asneeded. In some configurations in which weather stations 138 areprovided, stations 138 may also be provided with microphones 108 at ornear ground level (e.g., at 4 m height), and additional weathermonitoring stations 138 may also be provided at hub level on windturbines 102 to provide stratified weather data. Boundary microphones110 are also provided, each with an A/D converter 140 and a digitalsignal bus interface 142. In some configurations, extra microphones 114are provided as sensitivity microphones.

Data from reference microphones 108 and boundary microphones 110 is fedto a learning module 146, which in some configurations is a softwaremodule or program running in a computer such as computer 132. Morespecifically, data is fed to a signal analysis acoustic signaturerecognition submodule 148, which recognizes an acoustic signature ofeach wind turbine 102 in the noise picked up by microphones 108 and 110.

In some configurations, signature recognition submodule 148 utilizesfrequency spectrum and time signal analysis to recognize noisesignatures. Also, a control loop comprising a neural network module 156is provided from the neural network-based permanent far field noisemonitoring system comprising microphones 108 and 110 and, in someconfigurations, microphones 114 and/or weather stations 138 to each windturbine 102 for individual switching of wind turbines 102 innoise-reduced operation mode. However, weather stations 138 are notrequired in all configurations of the present invention.

Neural network module 156 is used in some configurations to recognize abackground noise signature as a function of time of year, a wind turbinenoise signature as a function of wind characteristics, and a differencebetween background noise and wind turbine related noise (emergence).Neural network module 156 is also configured to determine a transferfunction between specific wind turbines 102 and specific far fieldoutdoor microphones 110 and/or 114, as a function of windcharacteristics. The transfer functions represent propagation of noisein the atmosphere, which can be a function of the amount of water vapor,water droplets, temperature and pressure, pollution, etc. For example, atransfer function is established relating noise at a particular farfield outdoor microphone and a nearby wind turbine as a function of windspeed and direction, and, in some configurations, also of wind speedand/or direction variability (i.e., wind gusts). The use of permanentfar field noise monitoring ensures consistent control of noise emissionimpact. Monitoring noise in the far field is a first step used in someconfigurations before establishing a background noise signature.

In some configurations, noise picked up by sensitivity microphones 114is also considered in recognizing signatures, and this contribution isweighted by a weighting module 150 in accordance with a sensitivity mapincluding an indication of the most critical locations. For example, thenoise sensitivity map provides high weighting of noise (and thus highnoise control) in residential areas and low weighting (and thus lownoise control) in non-populated areas. In some configurations, thesensitivity map weighting varies as a function of time on anhour-by-hour basis, a daily basis, and/or a seasonal or monthly basis.Correlators 152 and comparators 154 may also be used as submodules inanalyzing the signals received by microphones 108, 110, and/or 114. Theanalyzed signal is applied to an adaptive self-learning neural networksubmodule 156, which determines a noise impact importance of individualwind turbines 102 at one or more locations 112 in a far field beyond aboundary 104 of wind park 100. For example, in some configurations,noise generated in residential areas where background noise is typicallylow and sensitivity to noise is greatest is weighted more heavily (i.e.,a lower threshold is set). In some configurations, noise occurringduring specific periods of time (e.g., evening hours) is more heavilyweighted. These and other weighting factors can be combined as necessaryto meet local requirements and/or sensibilities.

Noise immission is reviewed using far field microphones 110 and/or 114.Similarities are determined and comparisons 154 and correlations 152 aremade to determined correlation in the far field with the near field tofind noise impact via signature analysis. Also, neural network 156 willadvantageously remember the transfer functions. So over time, more andmore will be learned about the transfer function from emission toimmission and how noise in the far field relates to reference microphone108 and to wind turbine 102 physical locations. Thus, the transferfunction works as a map in some configurations rather than as just anumber, and is also correlated with physical positions of wind turbines102, reference microphones 108, and other microphones 110 and/or 114.

In some configurations of the present invention, signals from weatherstations 138 are also provided. These signals are provided to a weathermodule 158, which in some configurations is a software module or programrunning in a computer such as computer 132. Weather module 158 cancomprise one or more correlators 160 and/or one or more comparators 162,which can be used to process the data received for application to astratified atmospheric noise propagation model 164. Module 164, inconfigurations in which it is provided and used, provides input foradaptive self-learning neural network 156 to adjust the determined noiseimpact importance in accordance with atmospheric variations within aregion 122 (see FIG. 1) within which control of wind turbine noise is ofinterest. These atmospheric variations can include temperature and/orhumidity and/or wind variations and stratifications that can affect thepropagation of noise from wind park 100 to various locations. Thiseffect, which can vary from one location to another within region 122 insome cases, can cause variations on the order of 10 to 15 dB in theamount of noise propagated from wind park 100 to some locations withinregion 122. For example, certain types of temperature or humidityinversions may cause sound propagated from a wind turbine to be directedupward, reducing the impact at some locations. Other conditions mayincrease the efficiency with which sound is propagated, increasing theimpact of the noise. Weather conditions can vary locally within a smallscale, so a plurality of weather stations 138 at different locations andheights are provided in some configurations of the present invention.

When a maximum emission sound pressure is reached according to specifiednoise emission criteria, one or more wind turbines 102 are switched intoa noise reduced operation mode to meet the specified noise emissioncriteria. Noise emission criteria can include components related totypical periodic, impulsive turbine noise, and to typical noise emissionspectra of wind turbines. When a maximum noise emergence is reached at aspecific far field outdoor microphone, a corresponding nearby windturbine 102, for example, is switched into a noise reduced operationmode. More specifically, in some configurations of the presentinvention, wind turbine control instructions 172 are generated accordingto a determination of which, if any, wind turbines 102 are to operate ina noise reduced mode. Depending upon the noise impact assessment, whichmay vary not only in accordance with the actual noise generated by windturbines 102 but also with location, time of day, season, or year,weather conditions, background noise, etc., one or more wind turbines102 of wind park 100 may be selectively controlled by computer 136(e.g., by pitching the blades of the turbine) into a noise-reducedoperation mode. This mode reduces the electrical output of a windturbine 102 and hence, the total output of wind park 100. However, thereduction in electrical output in some configurations of the presentinvention is advantageously reduced by using a noise impact assessmentthat not only takes into account the actual noise generated by windturbines 102 but also noise impact location, time of day, season, oryear, weather conditions, background noise, etc. Moreover, using thenoise impact assessment to control individual wind turbines 102 of windpark 100 also reduces the amount of energy output that would otherwisebe lost to noise control, because the noise control need not affect allof the wind turbines 102 in wind park 100. Also, where it is determinedthat individual wind turbines 102 under known ambient conditionscontribute more to noise impact at certain locations than do other windturbines, those wind turbines can be targeted for noise-reducedoperation mode when those ambient conditions are determined to exist byneural network 156.

To assist in learning or in initially developing a library of noisemonitoring statistics 168, some configurations of the present inventionutilize sensitivity microphones 114 and an archive of site noiseprofiles 166 to develop transfer functions and learned input scenarios170 for adaptive self-learning neural network 156. For example, thetransfer function of noise emission from reference microphones 108 andboundary microphones 110 to various locations at sensitivity microphones114 can be determined as a function of wind, humidity, temperature,atmospheric stratification, etc. and used later to determine the noiseemission at these locations without using sensitivity microphones 114.In addition, knowledge gained by self-learning neural network 156 can beused at other installations by providing archived data 116 at the otherinstallations. Neural network 156 can be used in some configurations torefine site scenarios 170, transfer functions, and/or noise backgroundestimates based on local observations.

A “scenario” 170 can include extreme cases in which local conditionsstrongly affect the noise. Scenarios 170 in some configurations arebased upon yearly average propagation loss for a specific site, the windturbine layout vs. the average noise immission profile, (i.e., anaverage of all the transfer functions), and the extreme cases of minimumnoise impact and maximum noise impact. Average, minimum, and maximumimpact are used in some configurations. In new installations of windparks 100, all three scenarios can be provided. Others scenarios betweenthe extremes can also be used if available. If computing power is not anissue, all the recordings that are available can be used. Neural network156 learns and/or applies these scenarios to determine specific windturbine 172 control instructions. Stored scenario that are “most like” acondition that currently exists, or that happens most of the time, canbe used to determine the control instructions, if available. Forexample, if a wind is coming from the east, it may be more likely tohear certain of wind turbines 102 than if the wind is coming from otherdirections. If an east wind is prevalent over 75% of the year, theneural network will be take that into account.

Transfer functions and how they are related to physical wind turbinelayouts in a wind far can be memorized and archived 166. Once memorized,archive 166 from one wind park (or several) can be used at another windpark with a similar configuration, or a slightly different configurationfor which neural network 156 can again be used to find or refine thetransfer functions. Experience from wind park to wind park can becollected and centralized so that for the each successive installation,the noise reduction system will not have to start learning from scratch,but rather can use past experiences at other wind parks 100. A reductionin the total number of microphones 108, 110, 114 can be achieved byproviding a sufficient number of pre-learned transfer functions.

In some configurations, user event and mapping display 174, which may beend user terminal 134 (see FIG. 5) or another terminal, for example, canbe used to input special commands or instructions, including, forexample, areas 124 having high residential noise sensitivity, areas 126having enhanced residential noise sensitivity, and times of day, week,season, or year during which sensitivity is further enhanced.

In some configurations of the present invention, wind turbines 102 areswitched into noise reduction operation mode when a wind turbine noisesignature is recognized in the far field and that wind turbine 102 isrecognized to contribute to the far field emission receiver point orpoints (i.e., microphone locations).

The amount of energy reduction that would otherwise been needed isreduced by reducing wind turbine noise only during periods in which theimpact is quantified by noise emergence in the far field.

Advantage is also taken of the natural noise background of a site insome configurations of the present invention, so that wind turbines arenot switched into a noise reduction mode when the noise of the windturbines is masked by the natural noise background.

The noise emission control is adaptive, in that it effects a noisereduction only when a noise level is generated that exceeds specifiedlevels. Thus, less impact on energy production occurs than would be thecase if local ambient conditions that tend to reduce noise impact wereignored.

Estimates of the noise level in the far field of a wind park can takeadvantage of detailed, high-resolution knowledge of atmosphericconditions in some configurations of the present invention. Weatherpredictions, at best, are based on cubic mile cells of atmosphere, whichdoes not provide sufficient resolution of temperature gradients in someconditions to achieve the best noise control. However, non-uniformitywithin these one-mile cells results in influences that are or can besignificant with respect to noise propagation. For example, a 20 dBvariation in noise due to temperature alone might allow, as a rule ofthumb, approximately 20 dB more energy to be generated. If noisepropagation is curved downward due to a temperature inversion, noiseenergy that would otherwise escape into the atmosphere by rising wellabove the ground without being heard will be brought back to the groundand become heard. In this case, some configurations of the presentinvention command a noise reduction mode that reduces the noiseimmission. Noise reduction mode can be described as a changed bladepitch angle and/or the dynamic employment of any other blade specificdesign features with noise reduction capabilities (mechanical devices,surface treatment devices, chemical devices, electrostatic devices,etc.) As a rule of thumb (which can vary for different wind turbines), apercentage reduction in noise is approximately equal to a percentagereduction in electrical energy output. Thus, a 20 dB advantage gained byrecognizing temperature conditions that attenuate noise by 20 dB atground level may allow some configurations of the present invention togenerate generation of 100 times as much power (i.e., a 20 dB increase)from a wind turbine park 100. The full benefit may not always beachievable due to random variations in atmospheric and other ambientconditions, but the increased power generating capabilities provided bysome configurations of the present invention are likely to besignificant and may actually closely approach the “rule of thumb” levelunder favorable conditions. The greatest advantages may be achieved insituations in which a microphone 110 or 114 in the far field is at givena limit, e.g., 45 dBAs for a certain temperature level, and using aprior art method, all wind turbines 102 in wind park 100 are shut down.However, configurations of the present invention may not have to resortto shutting down a wind park 100 in this instance, because someconfigurations explicitly take into account atmospheric propagationeffects and ambient noise. For example, a tractor or a road near the farfield microphone may generate noise levels of 45 dBA or greater. Acontrol method based on using a fixed noise limit would completely shutdown the wind turbines because of this noise, even though it is not atall related to the wind turbine.

While the invention has been described in terms of various specificembodiments, those skilled in the art will recognize that the inventioncan be practiced with modification within the spirit and scope of theclaims.

1. A method for controlling noise from a wind park comprising aplurality of wind turbines, said method comprising: monitoring noiseemission from the wind turbines in at least a near field area; utilizinga transfer function of noise emission to determine a noise impactimportance of the wind turbines at one or more locations in the farfield area beyond a boundary of the wind park; determining which, ifany, wind turbines to operate in a noise-reduced operation mode inaccordance with the noise impact importance determination; andcontrolling operation modes of the wind turbines in accordance with thedetermination of which, if any, wind turbines to operate in a noisereduced mode.
 2. A method in accordance with claim 1 further comprisingutilizing an acoustic estimate of background noise to determine therelative noise impact importance of the wind turbines.
 3. A method inaccordance with claim 2 wherein the acoustic estimate of backgroundnoise is a function of at least one of time of year or time of day.
 4. Amethod in accordance with claim 2 wherein the acoustic estimate ofbackground noise is a function of at least one of turbulent wind groundnoise, vegetation wind noise, road noise, aircraft noise, agriculturalmachinery noise, industrial noise, and other extraneous noise sourcesnot related to the wind turbines.
 5. A method in accordance with claim 4wherein the acoustic estimate of background noise is derived using anacoustic signature signal analysis, wherein the analysis utilizesenergy, time, and frequency analysis to recognize and differentiatenoise sources not related to the wind turbines.
 6. A method inaccordance with claim 4 wherein the acoustic estimate of backgroundnoise is derived using an adaptive self-learning neural networkalgorithm that detects and qualifies acoustic transfer functions fromnear field microphones and far field microphones to dynamically andadaptively determine wind turbine individual noise contribution usingenergy, time, and frequency analysis.
 7. A method in accordance withclaim 4 wherein the difference between noise levels related to windturbines and extraneous noise levels not related to wind turbinessources is permanently evaluated.
 8. A method in accordance with claim 2wherein said determining a noise impact relative importance of the windturbines further comprises utilizing a noise impact map to weight thenoise impact importance as a function of spatial location.
 9. A methodin accordance with claim 8 wherein the noise impact map includes aplurality of critical noise sensitive areas in far field microphones.10. A method in accordance with claim 1 further comprising operating aplurality of wind turbines in a noise-reduced operation mode thatincludes sending control commands to each wind turbine based a primaryand a secondary acoustic target, wherein the primary acoustic target isthe difference between wind turbine related noise and all extraneousnoise levels and the secondary acoustic target corresponds to noiselevels within critical noise sensitive areas.
 11. A method in accordancewith claim 1 further comprising determining the transfer function ofnoise emission for each wind turbine utilizing reference microphones andat least one of boundary limit and far field microphones.
 12. A methodin accordance with claim 11 further comprising measuring noisegeneration using near field microphones positioned within the wind park,measuring noise impact using far field area microphones positionedoutside the wind park, and measuring noise impact using far fieldsensitive zone specific microphones.
 13. A method in accordance withclaim 11 further comprising determining an estimate of background noiseutilizing a spectral analysis algorithm configured to recognizeextraneous noise sources.
 14. A method in accordance with claim 13further comprising refining the determination of the transfer functionof noise emission for each wind turbine and the estimate of backgroundnoise.
 15. A method in accordance with claim 1 further comprising usingweather instruments to monitor weather data indicative of atmosphericvariations within a region within which control of wind turbine noise isof interest, and wherein said determining a noise impact importance ofthe wind turbines at one or more locations in the far field beyond aboundary of the wind park further comprises utilizing the weather dataindicative of the atmospheric variations and correlating with stratifiedatmospheric noise propagation effects in making said noise impactimportance determination.
 16. An apparatus for reducing noise impactfrom a wind park having a plurality of wind turbines, said apparatuscomprising: at least one site reference microphone and a plurality ofsite boundary microphones; and a computer configured to: monitor noiseemission from the wind turbines utilizing the at least one sitereference microphone and the plurality of site boundary microphones,utilize a transfer function of noise emission to determine a noiseimpact importance of the wind turbines at one or more locations in a farfield beyond a boundary of the wind park; and determine whether tooperate any said wind turbines in a noise-reduced operation mode inaccordance with the noise impact importance determination; and controloperation modes of the wind turbines in accordance with thedetermination of which, if any, wind turbines to operate in a noisereduced mode.
 17. An apparatus in accordance with claim 16 wherein saidcomputer further configured to utilize an estimate of background noiseto determine the noise impact importance of the wind turbines.
 18. Anapparatus in accordance with claim 17 wherein said computer furtherconfigured to utilize a signal analysis module to recognize noisesignatures of at least the wind turbines from extraneous noise sourcesnot related to wind turbines.
 19. An apparatus in accordance with claim17 wherein to determine a noise impact importance of the wind turbines,said apparatus further configured to utilize a sensitivity map to weightthe noise impact importance as a function of location.
 20. An apparatusin accordance with claim 16 further comprising weather data sensorsconfigured to indicate atmospheric variations in a region within whichcontrol of wind turbine noise is of interest, and wherein to determine anoise impact importance of the wind turbines at one or more locations ina far field beyond a boundary of the wind park, said computer furtherconfigured to utilize the weather data indicative of the atmosphericvariations and a stratified atmospheric noise propagation model inmaking the noise impact importance determination.
 21. A generatingsystem apparatus for generating electrical energy at a reduced far fieldarea noise impact, said generating system comprising: a wind park havingplurality of wind turbines; at least one site reference microphone and aplurality of site boundary microphones; and a computer configured to:monitor noise emission from said wind turbines utilizing said at leastone site reference microphone and said plurality of site boundarymicrophones; utilize a transfer function of noise emission to determinea noise impact importance of said wind turbines at one or more locationsin a far field beyond a boundary of said wind park; determine whether tooperate any said wind turbines in a noise-reduced operation mode inaccordance with the noise impact importance determination; and generatecontrol operation modes of said wind turbines in accordance with thedetermination of whether to operate any said wind turbines in anoise-reduced mode.
 22. A generating system in accordance with claim 21wherein said computer further configured to utilize an acoustic estimateof background noise to determine the noise impact importance of saidwind turbines.
 23. A generating system in accordance with claim 22wherein said computer further configured to utilize a neural networkalgorithm module to determine best noise reducing operations controlinstructions as a function of near field reference microphones inputsignal, far field boundary microphones output signals and learned noisereduction scenario.
 24. A generating system in accordance with claim 21further comprising weather data sensors configured to indicateatmospheric variations in a region within which control of wind turbinenoise is of interest, and wherein to determine a noise impact importanceof said wind turbines at one or more locations in the far field of saidwind park, said computer further configured to utilize the weather dataindicative of the atmospheric variations and a stratified atmosphericnoise propagation model in making the noise impact importancedetermination.
 25. A generating system in accordance with claim 24wherein said weather data sensors comprise both ground level weatherstations and hub level weather stations.